Artificial intelligence. Conceptual computer artwork of a human brain against a background of electronic circuitry, representing artificial intelligence (AI). The front of the brain is at right. AI is the ability of computers to learn and act independently of their original programming. Credit: Getty Images (Royalty Free).

We live in the age of artificial intelligence. A time where we're seeing technological advances that, even to just one generation prior, would have seemed like science fiction. In the marketing world, especially in dealing with data streaming in from all kinds of sources at an impressive (or exhausting?) rate, we now have various AI algorithms and advanced analytics tools at our fingertips.

Wow, what an exciting time it is! Or is it? Perhaps it is time we pause and reflect a bit on the so-called AI revolution in marketing.

AI has become one of those topics. You know, the topic that seems to dominate every panel discussion at every conference or trade show, comes up in boardrooms all the time and is the focus of many blogs, white papers and other forms of thought leadership content that have inundated your inbox, Twitter and Linkedin feeds. To me, AI has been the dominant topic in all of these domains–as well as in the business school classroom–for at least the last year or two, perhaps longer. It is baffling to me, though, that marketing as a profession still seems to have many more questions about AI than it has answers. That's not necessarily a bad thing, but it does give us pause for thought. When something has stayed on top of the hype cycle and professional/industry zeitgeist for seemingly so long, it is time to take a step back.

I'm bullish on the potential for AI to change how we think about marketing, how we interact and build relationships with our customers and business partners and how we efficiently and effectively spend our marketing budgets. But there's a catch. To unlock that potential we need to first clear the hurdle of seeing AI as a set of technologies algorithms and move to a more enlightened perspective. We need to move beyond seeing AI as that "shiny new toy" that it certainly could be, and go back to basics. Yes, basics. What do I mean by this?

Realize that AI is a means to an end. Nothing more, nothing less.

The technologies that we now collectively call AI (or in most applications, actually "merely" machine learning, although that's seemingly so 2016-2017!) are just that: technologies. We're talking about software, mostly. Algorithms. Yes, algorithms are changing our world, but they are computer programs that people wrote to accomplish certain tasks (as complex as the tasks could be).

Once we've gotten over the hype and excitement of AI, the next step in going back to basics is to start to identify where AI can deliver answers. Which questions do we want answered by AI-related methods and tools? In the context of marketing/customer analytics, I prefer to think of AI as we've thought of the practice of market research pretty much since the dawn of time. In market research, there are questions that businesses have that they then seek to answer through various research techniques. The rigor and discipline embedded in the practice of market research is valuable for marketers trying to unlock the potential of AI. The textbook approach is something we all are familiar with: (1) Start with some problem or challenge in the business. (2) Translate this into a set of research questions. (3) Use research methods–data collection, analysis–to give answers to the research questions that, upon being answered, provide insights into the problems/challenges that prompted the entire exercise. Where does AI fit into this? Evidently, it seems best-suited to the final phase, i.e., the research methods part.

AI in marketing analytics is best seen as a set of tools for answering questions.

If we think this way, which is very much a back to basics way of thinking, we quickly realize that the our AI and advanced analytics tools are ways of answering questions. Essentially, they are research methods (albeit very sophisticated ones). This puts them into the same class as, for instance, surveys and descriptive statistics, ethnographies and focus groups, and so on. AI then becomes a set of tools for answering important business questions. Indeed a potentially powerful set of tools, but nevertheless a set of tools. When seen this way, it forces us to think about what the questions are so that we can think about how to use our tools. And if we cannot come up with the right questions, then we won't have a need to use those tools. This can be powerful, because it helps us demystify AI. It sort of puts it in its place. It isn't to say that it is unimportant or that it is the same as the old technologies that came before it. Rather, it is a way to force ourselves to focus on solutions and answers instead of the shiny new toys in front of us.

Move towards a combination of human and artificial intelligence: HIAI

Marketing, particularly data-driven, analytics-focused practitioners, should be thinking of HIAI–Human Intelligence, Artificial Intelligence–as a hybrid approach. Prediction algorithms that form a lot of the AI tools that can be used for marketing analytics and customer insights are engineered to work well (given the right amounts of data at the right level of quality, of course). Eventually, if you work hard enough you'll get a pretty great prediction model out of machine learning and AI. But let's not forget about the humans! I think there's great potential in taking an HIAI approach in marketing analytics.

An example is a research project my team at Oxford Saïd has just started working on. We've teamed up with a large marketing agency that has a successful influencer marketing practice. We want to see how good AI can be at predicting who will be a top social media influencer using an impressive amount of data the agency has gathered on performance of prior influencers. I have no doubt that we'll generate models that will predict future influencer success very well. Our HIAI approach, however, is to combine human intelligence with the AI prediction models. In the business of influencer marketing, as in many others, there's a lot of human knowledge–learned over time through experience and innately known–that goes into "picking winners." We're going to run focus groups and interviews with agency professionals who are experts in an effort to codify their own "algorithms." When we combine this with our AI-based models, we expect to get even better outcomes. This is likely true in plenty of other business contexts. One example that comes to mind is in the music business, where record labels try to pick new acts to sign based on predictions about future popularity, sales and streaming. The BBC's Media Show podcast recently interviewed Rob Stringer, CEO of Sony Music. In the interview when talking about this he mentioned how data science, AI and algorithms are being used to help find new acts but that it hasn't replaced human judgment based on experience and taste; in other words, HIAI.In practical terms, I think the exciting potential for HIAI in marketing analytics/insights applications is that AI can be used to do much of the heavy lifting (especially in data-rich businesses) but HI–people!–can take this even further in terms of things like prediction accuracy and forecasting, and also add a layer of interpretability that only humans can provide.

Ultimately, as I said at the beginning, we live in an AI world. There's no turning back and that's perfectly fine. However, when we think of AI in marketing, especially in the related worlds of marketing analytics and customer insights, we are perhaps best served by going back to some foundational, basic principles. Ironically, by doing this we are likely to end up getting more out of these "new" technologies and advanced computer science that make up AI for analytics than if we continued to get caught up in the hype. The future of AI in marketing is exciting, provided we remember it is a tool to help us answer questions.